both of these approaches use NFAs under the hood, which means O(m * n) matching. our approach is fundamentally different: we encode lookaround information directly in the automaton via derivatives, which gives us O(n) matching with a small constant. the trade-off is that we restrict lookarounds to a normalized form (?<=R1)R2(?=R3) where R1/R2/R3 themselves don’t contain lookarounds. the oracle-based approaches support more general nesting, but pay for it in the matching loop. one open question i have is how they handle memory for the oracle table - if you read a gigabyte of text, do you keep a gigabyte-sized table in memory for each lookaround in the pattern?
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工具一览计划与执行：Obsidian + Claude CodeObsidian 是我的主要任务管理工具，让 Claude Code 读取 100 Days of SwiftUI 课程总览页面，就可以直接根据课程内容在 Obsidian 中生成一则 Tasks-100 Days of SwiftUI 待办清单，当中用：